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Towards Generating Novel Games Using Conceptual Blending Experimental AI in Games: Papers from the AIIDE 2015 Workshop Towards Generating Novel Games Using Conceptual Blending Jeremy Gow and Joseph Corneli Computational Creativity Group Department of Computing Goldsmiths, University of London, UK Abstract blending” (Schorlemmer et al. 2014)1. The project takes Goguen’s formalisation of conceptual blending as a starting We sketch the process of creating a novel video game by blending two video games specified in the Video Game point (Goguen 1999). Early implementations draw on the Description Language (VGDL), following the COIN- related notion of amalgams (Ontan˜on´ and Plaza 2010). The VENT computational model of conceptual blending. core ideas in the model are: We highlight the choices that need to be made in this Conceptual spaces may be mathematical theories, ontolo- process, and discuss the prospects for a computational game designer based on blending. gies, or, in our case, video game descriptions. Signature morphisms between conceptual spaces are Introduction mappings from the symbols of a source conceptual space into the symbols of target conceptual space. In the This paper outlines the process of creating a novel video diagram below, φ1, φ2, 1, 2, and ι are all signature game by blending two video game descriptions given in the morphisms. Video Game Description Language (VGDL). Several issues need to be considered: which games to blend? Which el- Generic spaces are a particular kind of conceptual space ements of the game will be blended and which removed? that possess maximum commonality with a number of Do we need to adjust the resulting game in order to make it given input spaces. In the diagram below, φ1 and φ2 rep- playable? Taking two games from the GVG-AI Competition resent the inclusion of the elements of the generic space (Perez et al. 2015), we provide a worked example based on G in both X and Y . a recent computational, domain-independant model of con- Blends are unique conceptual spaces that preserve structure ceptual blending (Bou et al. 2015). from the input spaces and a generic space G, via a specific Our account provides a high-level sketch of how a com- arrangement of signature morphisms; namely, such that putational system could blend game designs, and does not the diagram is commutative, and that ‘axioms’ in the input propose a concrete architecture. Instead, our intent is ex- spaces X; Y are sent to ‘theorems’ in the unique colimit plore how a game blending system could work. We high- B, known as the blend: light the specific challenges presented by various steps in the blending process, and describe how certain steps provide B the opportunity to add or close off new potential mechanics 1 2 and meanings. The implementation and evaluation of this X ι Y approach will be the focus of future work. φ φ 1 G 2 Background Within the COINVENT project the model has been Recent research (Treanor et al. 2012; Cook and Colton 2014) primarily applied to examples from mathematics (Bou has examined computational generation of games from pro- et al. 2015) and music (Cambouropoulos, Kaliakatsos- vided concepts. The current exploration of blending game Papakostas, and Tsougras 2014). The constituent processes descriptions is motivated by our observation that game de- have been automated to differing degrees, and for some sim- signers often build new games by combining ideas from ex- ple examples complete automation is feasible. One of the isting games. key outstanding issues is the evaluation of blends, both from Conceptual blending is the notion that new concepts can the point of view of judging the final outcome, and from an be generated by combining elements of existing concepts. ‘online’ point of view that can help constrain the potential COINVENT is an EU FP7 project that aims to develop combinatorial explosion as the collection of possible input a “computationally feasible, formal model of conceptual spaces is explored, and possible blends are computed. Copyright c 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 1http://www.coinvent-project.eu/ 15 VGDL: The Video Game Description Language allows Weaken the blend by removing/adjusting elements to the specification of simple arcade-style games (Schaul achieve viability. 2014). It was recently developed to support research into ar- Run the blend by elaborating the design to exploit any new tificial intelligence and games, in particular general game- structure which it has introduced. playing agents, and now forms the basis of the General Video Game Playing AI (GVG-AI) Competition (Perez et The following account is a thought experiment, but is, we al. 2015). argue, computationally plausible. We expect that the ma- VGDL conceptualises a game as having five components: jority of games developed by such a process would not be playable, and indeed there will be many partially devel- Sprite Set The entities in the game, their types and proper- oped blends that will fail. For non-trivial examples, each ties. The predefined types are Avatar, FromAvatar, NPC, stage presents a huge number of choices — a combinato- Static, Movable, Resource and Portal, plus a large number rial explosion which any automated designer needs to tackle of specialised subtypes, e.g. RandomNPC. The sprites are in order to extract playable needles out of a haystack of arranged in a hierarchy, where subsprites (here denoted ‘frankengames’. We draw attention below to some of the de- v) inherit types/properties of their ancestors. cisions a computational designer would have to confront. Interaction Set The mechanics of the game. Each interac- The term “designer” is used to refer to this imagined tion rule defines the consequences of a particular pair of agent, but one might also conceive a co-creative approach sprites overlapping. where a human designer takes some of the key decisions, and perhaps takes inspiration from the blends the system Termination Set The win/lose conditions, based on either produces. a timeout or the value of counter, e.g. player health. Previous work in automated game design has suggested Level Mapping A translation from ASCII characters to that there are few intrinsic criteria for game quality (Nel- sprites. son et al. 2015). Playtesting, whether by humans or bots, is an essential component of game design. An implementa- Level Designs Multiple levels can be defined in ASCII, in- tion could use feedback from automated playtesting of the terpreted using the Level Mapping. games to guide this process, as in e.g. (Nielsen et al. 2015a; A VGDL specification defines the first four elements, with 2015b). level designs being specified in a set of accompanying text We completely sidestep the difficult issues of blending files. level designs and blending audiovisual elements, focusing The sprites and rules are described in terms of a prede- instead on blending game entities and rules. Of course, the fined ontology, e.g. a sprite can be a RandomNPC which design of all these elements is intimitely connected, and a wanders around the screen. The default ontology is fairly full account of game blending would encompass these as limited, and the VGDL versions of well-known titles are of- well. ten quite simplistic. Nevertheless, it can be used to define an impressive range of games which present a serious challenge Selection for a general game-playing agent, as demonstrated by the The first question is which games should we blend? Input GVG Competition examples. In the context of game design, game selection can be thought of as taking part in some VGDL presents a large space of possible games. Random wider game design context: perhaps an agent is elaborating instances from within that space are unlikely to be coherant, a particular design, and decides to blend in another game playable games, let alone high quality ones (Barros and To- concept in order to fix a perceived problem with the orig- gelius 2014). Hence it also presents a research challenge for inal. Alternatively, an agent could be simply exploring the automated game design, and researchers are now beginning creative possibilities of mashing together diverse game de- to look at VGDL generation (Nielsen et al. 2015b). signs. For this paper, we have just hand-selected two games Blending VGDL from the GVG Competition example set: Frogger (AKA Taking VGDL specifications as our ‘conceptual spaces’ for Frogs) and Zelda (Perez et al. 2015). Both games are in- games, we wish to show how a computational agent could spired by well-known commercial titles and have simple conceivably create new designs by blending existing game specifications — the VGDL is shown in Figure 1 and 2 re- elements. Our proposed approach closely follows the gen- spectively. The level and visual design are both outside the eral COINVENT process model: scope of this paper, so for simplicity we have omitted the LevelMapping information and img and color prop- Select two input games to blend. erties. We have also renamed a few sprites and made minor Generalise the games by matching selected elements from alterations for clarity, without affecting the game mechanics. each, resulting in a generic game concept. It is possible to intuit a lot about how these games work from the VGDL descriptions: the player controls an Avatar, Blend the games by combining their constituent elements, Missiles travel in given direction etc. Due to lack of space, reformulated in terms of the generic game concept. we do not include a full description of the games: see Schaul Evaluate whether the (perhaps nonsensical)
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